Firstly, we introduced the development of cloth simulation in recent years. Based on physical model of cloth simulation, we established the simulation system with a simplified mass-spring model. The computational efficiency is increased with this model. A modified implicit method was proposed in this paper. This method produces plausible animation, and it is easy to be realized with a stable and good real-time performance. The paper adopted AABB (Axis-Aligned Bounding Boxes) bounding volume approach for the detection of cloth collision, it obtains an excellent real-time effect of cloth simulation.

The key point of trajectory basis based Non-Rigid Structure from Motion (NRSFM) is how to tune the basis size and how to choose the best basis combination. If the basis size is too small, the trajectory is poorly represented by the basis. But too large basis size makes the system more ill-conditioned and the reconstruction error becomes unbounded. In this paper, an automatic method is proposed to select the trajectory basis, which can select appropriate basis size. The experiment results show that, compared with the empirical value from a lot of repeated experiments, the proposed method can improve both reconstruction accuracy and efficiency of NRSFM.

The problem of noninvasive computing the epicardial surface potentials from torso surface potentials constitutes one form of the inverse problem of ECG, which can be acted as a regression problem with multi-input and multi-output. In this study, the SVR method is invoked to predict the inverse solutions, which compared with the common regularization methods. To build an effective SVR model, the hyper-parameters of SVR are set carefully by using the grid search optimization method. The experiment results shows that SVR method is an effective way for solving the inverse ECG problem, which can reconstruct more accurate epicardial surface potentials distribution than the common regularization method, such as Tikhonv method and LSQR method.

In this paper we use the method of MRF and neural network to solve the problem of parameters estimation in non-rigid 3D movement. Firstly, the method of MRF is used for modeling the local motion correlation of each feature point, and the 3D coordinates of each feature point are obtained. Then the method of neural network is used for clustering the feature points according to their motion situation. When the neural network reaches stabilization, we can get the motion parameters of each feature point. Finally, we correct the neighborhoods of each feature point according to motion parameters. The experimental results show that our algorithm can correctly estimate the non-rigid motion parameters.